the effect of political intervention of the government … · 2. this paper attempts to analyze the...

Journal of Economics and Economic Education Research Volume 20, Issue 3, 2019

1 1533-3604-20-3-165

THE EFFECT OF POLITICAL INTERVENTION OF THE

GOVERNMENT ON THE COST EFFICIENCY: CASE

STUDY OF IRAN’S BANKING SYSTEM

Ali Dehghani, Shahrood University of Technology

Amir Hossein Ghaffari Nejad, University of Semnan

Jafar Abbasi, University of Semnan

ABSTRACT

The purpose of this paper is to investigate the government political intervention in the

banking system, seeking to answer the question of whether the continuation of this intervention

has been accompanied by cost-efficiency. For this purpose, the efficiency values of Iranian banks

have been obtained using the stochastic frontier analysis method in panel data and defining a

translog cost function and the trend of the efficiency has been analyzed. The data used for the

analysis included the information of 29 state-owned, private and privatized banks which were

active in Iran's economy from 2008 to 2016. After estimating the cost function and obtaining the

efficiency values, the effect of government intervention on the efficiency has been investigated

using the econometric analysis with panel data. Meanwhile, due to the critical conditions of

Iran’s economy in the 2010s and its effect on the banking system, some of the variables

representing the recession have been considered among the explanatory variables. The results of

the estimations indicate that after the currency shock in 2013, the cost efficiency of the banking

system was reduced and the government intervention had a negative effect on it. This effect was

more severe in the state-owned banks.

Keywords: Government Intervention, Cost Efficiency, Panel Data.

JEL Classification: G28, D61, C23.

INTRODUCTION

At the time of the inefficiency of the market system, government intervention in the

economy is done with the goal of balancing social costs and benefits. Meanwhile, financial

markets as the pillars of the economic life of any country are very important to the governments.

Under the condition of inefficiency in financial markets, and especially in the event of financial

crises, governments’ Intervention and support are often increased. Government intervention in

financial markets is not limited to periods of systematic crises and social turmoil such as the

Great Depression. Intervention in financial markets can be a combination of nationalization acts,

Bail-outs, voting for directors and takeover during times of crises (Pagano & Volpin, 2001). In

the face of crises such as the 2008 US crisis, capital injections are made by the government in the

financial sector. Capital injections in the banking sector, as the most important part of financing,

usually occur in state-owned or semi-state-owned banks. Capital injections significantly affect

the balance sheet and financial statements of the banks which in turn, affect the performance and

the efficiency. In order to promote privatization, measures are taken by governments to reduce

government debt, increase the efficiency of state-owned companies and promote competition in


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the monopoly sectors of the economy (Megginson & Netter, 2001). Sometimes, the privatization

plan is carried out for political purposes which have higher costs for the society. Besides,

privatization may not reduce the government intervention in the post-privatization period than

before (Perroti, 1995). Government intervention is such that politicians and bureaucrats can use

state-owned companies for their own personal and political goals; they may even encourage

private companies to receive state subsidies, thereby increasing the level of their intervention in

the economy. But private bank owners are likely to seek to reduce the costs of government

intervention in their bank. Also, in the issue of recruitment in a state-owned bank, there is the

possibility of intervention and, as a result, political corruption to the benefit of particular groups

(Shleifer & Vishny, 1994); however, mentioned corruption can also occur in privately owned

banks (Clarke et al., 2005).

1. Contrary to the prevailing belief that intervention is considered to be a factor in reducing the efficiency of the

banks, some have seen better performance among the state-owned banks, since the political intervention

under reasonable conditions (the existence of a complete market) may be better than the existence of an

oligopolistic monopoly structure in the banking sector, even If politicians seek to secure the goals of specific

groups and spread their social popularity by increasing recruitment in these banks. In terms of some

economic variables such as employment, such intervention may even be better than the market solution

(Willner, 2001).

2. This paper attempts to analyze the effect of government intervention on cost efficiency, taking into account

the state ownership and the formed crises in Iran’s banking system in the 2010s. The issue of government

intervention in Iran’s banking system is beyond the issue of ownership, since the intervention in privatized

banks still exists, as in some cases, government financing is carried out solely through the privatized banks.

Also, in recent years, the Iran economy’s exposure to stagflation condition, along with the institutional

shortcomings of the Iran financial market led to the emergence of wide disruptions in the balance sheet of the

banking system, and provided the basis for the mismatch maturities of banks' assets and liabilities. This has

led banks to resist interest rates on term deposits set by the central bank. Due to the new resource allocation

to meet previous commitments with the past depositors, resulting in the withdrawal of a significant part of the

supply resources from the lending cycle, a credit crunch was formed. Therefore, government intervention

should be examined in the context of the current crisis and its effect on the cost efficiency of the banks

should be considered.

3. This paper is organized in 6 sections. After the introduction, the literature on the research will be reviewed. In

the third section, we will examine the status of the government intervention in the banking sector of Iran. The

fourth section contains the paper methodology in which empirical models will be introduced to estimate the

cost efficiency and analyze the effects of government intervention on the efficiency with an emphasis on the

banking system crisis. In Section 5, the results of the estimations and in section 6, conclusion and suggestions

will be presented.

LITERATURE REVIEW

Historically, no industry has been subjected to intense intervention by government as much

as the financial industry (Haber & Perotti, 2008). Such an approach from the government to the

financial sector has been known as "Financial repression," and it is a controversial issue in most

countries, especially since the 1970s. Mathieson & Mckinnon (1981) define the term of financial

repression as a set of policies aiming at generating income from the financial system through

government intervention in pricing and allocating the resources of the granted funds and

determining real interest rates. Giovannini & Demelo (1990) also refer to financial repression as

a combination of imposed restrictions (price and amount) on the domestic sector and controlling

the flow of international capital to reduce the cost of domestic financing and as a source of

income for the government. So far, there have been many criticisms of the policies of financial

repression and numerous deviations that these policies create in the financial markets. However,


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many governments in developing countries insist on continuing these policies, under the pretext

of increasing investment and economic growth.

In the context of studying the effect of government intervention on banking efficiency,

there are various categories on both aspects of government intervention and the use of type of

efficiency in studies. The type of government intervention is considered as three general

categories in various studies, namely (1) ownership, (2) allocation of credits, and (3) suppressing

interest rate. Also, in terms of using the type of efficiency in the studies, regardless of how it is

estimated by parametric or nonparametric methods, two general categories can be identified: (1)

efficiency in performance and (2) efficiency in production or cost. In most of the empirical

studies, the financial ratios of the CAMELS system have been used for the performance variable

as an early warning system for assessing the health status of the banking network and each bank

in a country. For this purpose, the criteria of capital adequacy, asset quality, management quality,

profitability, liquidity, and sensitivity to market risk are considered as indicators of performance

evaluation in these studies. Variables such as return on assets (ROA) and return on equity (ROE)

are among the most important performance indicators in the field of profitability in the studies.

Efficiency in the production or cost is significant, in that the goal of an enterprise is to maximize

production or minimize costs, which both approaches will ultimately lead us to the same solution

(Henderson & Quandt, 1971).

Several studies have focused on state ownership or the role of privatization in improving

efficiency and in their modeling; they have mainly concluded the negative effect of the

intervention on efficiency. Most of these studies have introduced ownership as a dummy variable

in their model. Studies such as Xiaoqing Maggie & Heffernan (2007); Jiang, et al. (2009),

Figueira, et al. (2009); Pasiouras, et al. (2009); Jiang et al. (2013) have estimated the efficiency

of the banks positively. In the meantime, few studies such as Berger, et al. (2009); Tecles &

Tabak (2010) have found positive effects on the issue of intervention; the main reason for this

conclusion is the large size of state-owned banks and the economies of scale.

In the context of government intervention in the allocation of credits, some economists like

Mankiw (1986) considered it vital and believe that these credits are guaranteed by the state, so

that intervention can be effective in improving efficiency and performance. In this respect, he has

extracted the optimal level of effective intervention. But Gale (1989) regards this intervention as

a subsidy for the people who are unable to finance their investment and considers it probable to

lead to inefficiency. This intervention can be accompanied by an increase in asymmetric

information (more adverse selection and moral hazard). Meanwhile, the majority of empirical

studies, such as Bokpin (2013); Hryckiewicz (2014); Torres et al. (2016) have analyzed the

empirical relations in the context of government intervention in allocating credits in the form of

state ownership issue. The results of these studies indicate that banks are inefficient and

ineffective. Undesirable performance has been observed in the increasing of non-performing

loans and increased financing costs. Therefore, in the area of credit granting, the effect of

government intervention has been more severe in the state-owned banks.

The suppression of interest rate as a result of government intervention is one of the

measures imposed by legal restrictions, such as the setting of maximum interest rate (Espinosa &

Yip, 1996). The advantage of such an action is that the government can distribute its debts at a

low interest rate, or even repay them. The suppression of nominal interest rates and the formation

of a real negative interest rate will lead to a reduction in the real value of government debt

(Reinhart & Sbrancia, 2011). The results of most empirical studies (Hermes & Nhung, 2010;

Barrell et al., 2017; Yao & Eugene, 2018) have shown the negative (positive) effect of


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suppressing interest rates (financial liberalization) on banks' efficiency or performance. They

claim that financial liberalization has reduced the effects of the financial crisis on performance.

Of course, Hermes & Meesters (2015), by their own modeling, concluded that the positive effect

of financial liberalization on the efficiency of the banks is conditional on the quality of

regulations and supervision over banks.

Government Intervention in Iran’s Banking System

During several periods, one of the most important challenges facing Iran's economic

development has been the government intervention in economic enterprises, among which the

banking sector has been no exception. After the 1979 revolution, ownership type of the banks in

Iran was changed to state ownership. At that time, if there was a problem in the banking system,

the government would overcome the problem with its support and injection of oil revenues.

Then, the government planned for the privatization of business enterprises in Iran in order to

increase the efficiency of the enterprises, empowerment of the private sector, promotion of

competition, expansion of the capital market and downsizing the state. As a result, private banks

entered into Iran’s banking system since 2001, and 80% of the shares of the state-owned banks

have been gradually transferred since 2006.

But due to the recession in the 2010s, which resulted from the intensification of sanctions

and inappropriate implementation of the distribution system of cash subsidies, the government

budget deficit in recent years has been increasing and the government began to finance its budget

deficit from the resources of the central bank and other banks. The result was a sharp rise in

government debt to the banks and the central bank and so government debt became the major

part of the banks’ assets. Since the government has not paid its debt, this part of the assets will

actually flow out of production. An investigation of central bank data shows that the government

debt to the banking system has increased from 910 billion Rials in 2012 to 2197 billion in 2016,

so that the government debt to the banking system during the years 2012-2016 was about 2.4

times higher and its average annual increase rate has been 24%. In Figure 1, the upward trend of

the government debt to the banking system is visible.

FIGURE 1

GOVERNMENT DEBT TO IRAN’S BANKING SYSTEM Source: https://www.cbi.ir.

291.5394

364.6339

553.4035

638.4761

910.4

1109.6

1466

1738.6 2197.5

0

500

1000

1500

2000

2500

2008 2009 2010 2011 2012 2013 2014 2015 2016

10

00

bili

on

Ria

l


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However, in order to investigate the government intervention, given the inflationary

situation which Iran has always faced with and two-digit inflation over the past decades resulting

in the increase in the value of banking system assets, it is necessary to present the upward trend

of the government debt to the banking system in proportion to the assets of the government.

Thus, in Figure 2, the trend of the ratio of received credits of the government and state-owned

companies from the banking system to the volume of assets is presented as an indicator of

government intervention in the banking system. Government intervention in the banking system

has been increasing in the course of the review period (according to the latest available

information).

FIGURE 2

GOVERNMENT INTERVENTION IN IRAN’S BANKING SYSTEM

Source: https://m.theglobaleconomy.com/

Memo. The linear trend has been obtained by using Hodrick-Prescott filter.

Another aspect of the government intervention in Iran’s banking system is the

determination of certain interest rates by the Central Bank of Iran, which is sometimes indicative

of political preferences rather than economic realities. This action has the effect of eventually

endangering the banks and facing them with a significant amount of non-performing loans. Also,

due to the higher interest rates on bank loans than interest rates on deposits, some individuals and

firms have been motivated in recent years to receive loans from the banks, and then deposit the

same in the banks and benefit the difference. As a result, bank loan turned into rent and in the

end, many unqualified individuals and firms received bank loans which led to overdue claims.

There is another point regarding the determination of certain interest rate by the central bank. In

recent years, due to the Iran’s economy facing with stagflation condition and the consequent

institutional shortcomings of the Iranian financial markets, large disturbances have emerged in

the combination of the balance sheet of the banking system which provided a background for

mismatch maturity of the banks' assets and liabilities, and continued the conduct of the Ponzi

game. In fact, running the Ponzi game by the banks is because the mortgage loans have not been

repaid on the due date and, on the other hand, banks are obliged to pay the interest to the

depositors, which in turn, has caused them to be trapped in the Ponzi game. Therefore, the

determination of the interest rate on time deposits was accompanied by non-observance of the

determined ceiling by the central bank. The continuation of this non-conformity behavior may

provide the possibility for disregarding justice in the resource allocation and creating a

5.0%

6.7%

5.7%

7.0%

9.9%

9.9%

12.5%

13.3%

15.2%

0%

2%

4%

6%

8%

10%

12%

14%

16%

2006 2007 2008 2009 2010 2011 2012 2013 2014

Government Intervention trend


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significant gap between potential and actual growth from the channel of increasing the idle

capacity of the economy (Ghaffari nejad et al., 2018).

Therefore, the wide-ranging intervention of the government in the banking system has had

many consequences for Iran’s banking system in different aspects, as the financial stability of the

system in the future has become a serious challenge for the authorities and researchers in

banking industry.

METHODOLOGY

Empirical Methodology

The measurement of efficiency in the banking industry has an extensive literature in which

various calculation methods have been used. Major studies in this area have applied data

envelopment analysis (DEA) and stochastic frontier analysis (SFA) methods. DEA is a

nonparametric method that uses data from all samples and solves problems using mathematical

programming models. The SFA method is one of the commonly used parametric methods in

numerous studies in which the stochastic production function is independently estimated with the

statistical parametric method by panel data as well as cross-sectional data. The strengths of the

SFA method, such as the consistency with statistical disturbances like the size of the error and

other factors outside the control of the enterprise, no need for price information and the

possibility of conducting the hypothesis test, have led to the relative advantage of this method in

applied studies with econometrics (Coelli, et al., 2005; Lan & Lin, 2003). So far, many studies

have been conducted on the subject of the efficiency of the banking industry using both

nonparametric and parametric methods. Most of the studies in this area, such as Rangan, et al.

(1988); Aly et al., (1990); Kaparakis, et al. (1994); Dietsch & Vivas (2000); Battese, et al.

(2000); Fries & Taci (2005); Bonin, et al. (2005); have been conducted with the main goal of

calculating efficiency, and usually, the results of two parametric and nonparametric methods

have not been the same. Some of these studies, such as Sheldon (1994) & Beccali (2004), have

investigated the adaptive comparison of the efficiency of both above-mentioned methods

considering the same clarification for inputs, input prices and outputs. The results of the studies

about these methods are very different, in that in some studies, the calculated efficiency in both

methods is the same while in some other studies, there is significant difference between the

results. Despite the extensive use of nonparametric methods, econometricians consider them

inefficient in efficiency-based studies. The most important reason for this opinion is the nature of

definiteness of these methods, in which the results are very sensitive to the outliers and

measurement errors (Cazals et al., 2002). Also, considering that consistency is usually more

important than other attributes of estimators in econometrics (Pindyck & Rubinfeld, 1988), most

economic studies tend to use methods based on frontier analysis.

Hence, in this paper, the main variable under review is the cost efficiency derived from the

stochastic frontier analysis (SFA) method in panel data, which is proposed by Greene (200

YAO,b). In this approach, Greene considered unique intercepts for each cross-section in the

stochastic frontier model with panel data, which is unlike the previous models presented in SF

approaches by Pitt & Lee (1981); Battese & Coelli (1988). In terms of formula, Greene

introduced the following relation:

'it t ït itY X

(1)


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This model, in comparison with previous models, allows the time varying inefficiency to

be separated from the unobservable heterogeneity at any cross-section which is fixed in time, but

in Pitt and Lee's model (1981), it was assumed that the inefficiency in all homogeneous cross-

sections is fixed:

2

2

'

(0, )

(0, )

it it it

it it i

it

i u

Y X

u

u

(2)

In (2), ui is the inefficiency which is fixed over time, but in the Greene’s model,

inefficiency is time varying. Greene estimates the model (1) with two methods for estimating

fixed effects and random effects in a model with panel data using the maximum-likelihood (ML)

estimator. While the model with random effects in this approach can easily be estimated by

simulation methods, estimating the maximum-likelihood of fixed effects requires considering

two important issues in relation to models with nonlinear panel data. The first problem is purely

computational due to the large dimensions of parametric space. However, Greene showed that

the approach of maximum-likelihood dummy variable (MLDV) is computationally possible,

even in the presence of a large number of nuisance parameters α_i (N>1000). The second is

incidental parameters problem which occurs when the number of crosse-sections is relatively

larger than the length of the panels. In such situations with N→∞ and fixed periods (fixed T),

Intercepts (constant components) are estimated inconsistently because Ti observations are used

to estimate the specific parameter of each cross (Neyman & Scott, 1948; Lancaster, 2002).

As Belotti & Ilardi (2012) have shown, the variance of parameters is affected because of

this inconsistency and will lead to inefficiencies in the estimation. It seems that the MLDV

approach is only appropriate when the length of the panels is large enough (T≥10). Therefore,

model (1) is the most flexible and parsimonious choice among several specifications of time

varying models.

In order to estimate the SFA-based regression, the following trans log cost function has

been developed based on the studies of Koutsomanoli-Filippaki, et al. (2013) and Jiang, et al.

(2013). Based on the estimation results of this function, the efficiency of Iranian banks is

obtainable.

3 2

13 31 1 1

3 3 2 2

3 31 1 1 1 1 1

3 2

31 1 1

ln( ) ln( ) ln( ) ln( )

1 1 1ln( ) ln( ) ln( ) ln( ) ln( ) ln( )

2 2 2

ln( ) ln( ) ln(

i k ri i k r

i k r

ji k m srij km rs

i j k m r s

i kik ir

i k r

Y w ZTC Tw TA TA w TA

YY w w ZZTA TA w w TA TA

Y w YTA w

3 2

31 1 1

2

2

) ln( ) ln( ) ln( )i kr rkr

i k r

it it

wZ ZTA TA w TA

T

(3)

In equation (3), TC is the total cost, TA stands for the total assets and Wk are the input

prices (w1: price of financial resources; w2: price of labor; w3: price of physical capital). Yi are

the outputs and Zr is the quasi-fixed input, in which equity is considered as the quasi-fixed input;


8 1533-3604-20-3-165

besides, net loans and deposits are used as model outputs. T, ηit and εit indicate time trend,

inefficiency and random error, respectively and α, β, ψ, Φ, ρ, τ, ϖ, κ and σ are model parameters.

In this paper, the efficiency obtained from the estimation of equation (3) is used as the

dependent variable in examining the relationship between government intervention in the

banking system and the efficiency of the banks. The empirical model of efficiency effects

adapted from the study of Hou et al. (2018) will be considered as follows:

6 2

0 1 2

1 1

int ( int )it it it it i it j t i t it

i j

Eff P ervin GOV P ervin X A

(4)

In equation (4), the t and i indexes represent time and cross-sections (banks), respectively.

The β values are model parameters. The υi ηt and λi indexes indicate unobservable effects in

time and cross-sections, respectively. It is assumed that apart from the disturbance (ζit), the

equation has a standard normal distribution and lacks serial correlation. Effit as the dependent

variable of the model is the efficiency that will be obtained from the estimation of equation (3).

The Pintervenit variable is the government political intervention, and GOVit is the dummy

variable of the state ownership structure of a bank. Xit is the vector of control variables that

represent the bank characteristics; At is the vector of macro variables that contain time series

observations and show the environmental effects on the efficiency of the banks.

Definition of Variables and Data

The source of the data used in this study is the annual statistics published by the Iran

banking Institute in which the time period 2008-2016 is considered, according to available data.

In the meantime, the information of 29 Iranian private, state-owned and privatized banks have

been used. These banks have been selected based on the completeness of their information

during the period under review. Furthermore, data on macroeconomic variables have been

extracted from the central bank's economic indicators.

In Table 1, the explanatory variables used in estimating the regression equation (4) are

presented. The variables introduced in rows 3 to 8 are the bank-specific characteristics and in

rows 9 and 10, the macroeconomic variables are presented.

Table 1

EXPLANATORY VARIABLES AFFECTING EFFICIENCY

Row Explanatory

Variable Symbol

Theoretical Expectations

Type of

Impact Description of Impact

1

Government

political

intervention

Pinterven -

According to available information, the ratio of the volume of

claims on the government to the total assets of each bank is

used as a proxy of the government political intervention.

According to the literature reviewed in the previous sections

of the paper, government intervention (getting bank credits

by the government) is expected to have a negative impact on

the cost efficiency of the banks.

2

Government

political

intervention in

state-owned

GOV

Pinterven -

The purpose of using Gov dummy variable is to determine

whether government intervention in the state-owned banks

has a higher negative impact on the efficiency compared to

the non-state-owned banks.


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Table 1

EXPLANATORY VARIABLES AFFECTING EFFICIENCY

Row Explanatory

Variable Symbol

Theoretical Expectations

Type of

Impact Description of Impact

banks

3

concentration on

the loan

(Herfindahl-

Hirschman

Index)

HHI ?

This variable indicates the concentration on the loan granting

in the banking system and is obtained by calculating the sum

of the squares of each bank's share of the total banking

network loan. It is expected that by concentrating on the loan

granting, the cost efficiency of the banks will be higher due

to improved bank outputs. However, the impact will be

negative under the condition that loan repayments are

delayed and turn into non-performing loans.

4 non-productive

assets ratio NPR -

This variable is defined as proxy using the ratio of the fixed

assets to the total bank assets; possessing high volumes of

non-productive assets is an indication of not getting output

from these assets. This will challenge the cost efficiency of

the bank, since the bank has to use its other limited resources

when considering the expenses.

5 Cost to revenue

ratio COTRE -

As a measure of profitability, the increase of this ratio will be

an indication of reduction in profitability and is expected to

have a negative relationship with cost efficiency.

6 Liquidity ratio Liq ?

The ratio of the liquidity to the bank assets has been used in

several studies. The increase in the willingness to retain

assets in cash results in the decrease of loan granting, thus

reducing revenue and efficiency. However, low amounts of

this ratio endanger the stability of the financial system, so the

monetary authorities set obligations in this regard. Low ratios

will also challenge the bank expenses.

7 Bank size size ?

This variable is calculated based on the logarithm of the total

assets. It is usually expected that increasing the size of the

bank will lead to the economies of scale and have a positive

effect on the cost efficiency. However, the large size of the

bank will sometimes result in reduced efficiency by creating

complexities in the management of the bank. In different

studies, three positive, negative and ineffective effects have

been obtained.

8 Non-interest

income NII ?

This variable indicates the relative importance of the fee-

based services. The increase in the share of such revenues is

an appropriate source of revenue, given that there is no need

to divide them between depositors. However, relying too

much on these revenues will deviate the bank from its main

mission and lead to inefficiencies.

9 The inflation rate INF ?

Inflation will increase the bank expenses through the channel

of the inputs by increasing various types of rates. On the

other hand, it will increase revenues by increasing loan

interest rates. Therefore, it is possible to consider various

effects for it.

10

The

unemployment

rate

Unem -

The rise of unemployment, which is typically the result of a

recession in the economy, will lead to an increase in non-

performing loans and a decrease in loan demand by reducing

the total income of the society. These, in turn, will reduce the

cost efficiency of the banks by decreasing the bank outputs.


10 1533-3604-20-3-165

Estimation Results

According to the methodology section, the Trans log cost function is estimated using the

SFA method in panel data. This model has been estimated using both fixed and random effects

according to the Greene's approach (2005a) and the Hausman test has been used to make the

final decision about choosing the better method. The results of the Hausman test presented in

Table 2 confirm the superiority of the fixed effect method.

Table 2

HAUSMAN TEST RESULTS

Test The statistics Result

There are fixed effects **159.93 Housman

*,

**, and

***, indicate the significant levels of 10, 5 and 1 percent, respectively.

After estimating the equation (3), the cost efficiency of the banks has been obtained. In

Figure 3, the trend of average annual cost efficiency of Iranian banks is visible. As shown in

Figure 3, during the review period, the efficiency has been on an upward trend from 2008 to

2013 and on a downward trend in the period 2013-2016. This can be attributed to the effects of

the intensification of banking sanctions on Iran and the onset of a recession in Iran’s economy

since late 2012. The spread of the recession in Iran’s economy, which has been accompanied by

a credit crunch in the banking system, has reduced the cost efficiency of the banks due to the

disturbances in their outputs.

FIGURE 3

THE TREND OF AVERAGE ANNUAL COST EFFICIENCY OF IRANIAN BANKS Source: Authors' calculations. *The efficiency has been obtained from the estimation of the translog cost function (Equation 3).

In order to estimate the model demonstrating the effect of the government political

intervention on the cost efficiency of the banks, the obtained amount of the cost efficiency is

used as the dependent variable in the estimation of the equation (4). Regarding the review period

2008-2016, which contains 9 years, there is no need to examine the stationarity of the variables,

0.69

0.82 0.80 0.77 0.78 0.85

0.80 0.76

0.58

0

0.2

0.4

0.6

0.8

1

2008 2009 2010 2011 2012 2013 2014 2015 2016


11 1533-3604-20-3-165

because stationarity is checked for long periods of time. In Table 3, the results of all the

estimations of the relationship between the government intervention and the government

efficiency using the fixed effects method are presented. In all the estimated models, the F

statistic is significant and indicative of the existence of relationship between the hypothetical

explanatory variables and the dependent variable (efficiency). The Significance of F-Leamer also

indicates that the cross-sections (banks) are not homogeneous. Hausman statistic is used to test

the existence of fixed or random effects in the cross-sections. The significance of Hausman

statistic confirms the use of the fixed effects method for the estimation.

Regarding the effect of control variables, the results indicate that in all the estimated

models, the concentration index has a positive effect on the cost efficiency of the banks, which

means that by increasing concentration on the market, the monopoly power is formed for the

banks and this power leads to an increase in the cost efficiency. The nonproductive assets of

Iran’s banking system, which increased during the 2010s due to the sanctions and the stagflation

condition, have reduced the cost efficiency. The negative effect of the cost-to-revenue ratio

refers to the fact that banks with higher efficiency tend to have better cost control while looking

for opportunities to increase revenue.

Table 3

MODEL ESTIMATION RESULTS

Variables Model(1) Model(2) Model(3) Model(4)

Pinterven -4.75824***

-4.77392***

-4.8418***

-4.77565***

Govpin -5.2473***

-5.27539***

-5.42397***

-6.22008***

HHI 0.167362***

0.184203***

0.184852***

0.217808***

NPR -0.961**

-1.00251**

-0.94348**

-0.98473**

COTRE -0.39415***

-0.33896 -0.34634 -0.33145

Liq 0.252787 **

0.271211**

0.279107**

0.446448***

Size -0.01643 -0.01576 -0.03351

NII -0.27875

INF -0.06566 -0.06661

Unem -0.02988**

-0.03365**

-0.03649**

-0.04019**

Constant 1.136839***

1.292528***

1.333101***

1.539978***

F_test 4.08***

3.59***

3.19***

3.75***

F (leamer) 2.3***

2.3***

2.27***

2.5***

Hausman 20***

23.35***

21.3***

36.57***

*,

**, and


As shown by the results of all the estimated models presented in Table 3, the government

political intervention has a negative effect on the efficiency of the banks, and this effect increases

for the state-owned banks. These results are contrary to the results of the studies such as Berger,

Hassan & Zhou (2009); Tecles &Tabak (2010) that considered the cost efficiency higher in state-

owned banks. Therefore, all the estimation results are compatible with the results of most

studies, such as Xiaoqing Maggie & Heffernan (2007); Jiang, et al. (2009); Jiang, et al. (2013);

Hou, et al. (2018).

The positive effect of the liquidity ratio means that possessing enough liquidity by the bank

will not challenge it concerning the expenses. In all the estimated models in which the size of the

bank has been used among the explanatory variables, the effect of this variable is meaningless on

the cost efficiency. This resulted lack of significance is compatible with the results of the studies

such as Berger & Mester (1997); Pi & Timme (1993); which concluded that larger banks would


12 1533-3604-20-3-165

not have any advantage in terms of efficiency. The non- interest income ratio, which is

considered as an explanatory variable only in the model (4) in Table 3, has a negative effect on

the efficiency, which means that by increasing the share of such revenues, the bank deviates

from its main area of activity, and therefore the cost efficiency is reduced.

Furthermore, in relation to the effect of macroeconomic variables, the results showed that

the inflation has not had a significant effect on the cost efficiency; indeed, the effects of the

inflation on the rate of the use of inputs and the interest rates have offset each other. Especially

in recent years in which the central bank has been determined to reduce the inflation and stabilize

it at a single digit rate, the inflation has not been an important factor in making macroeconomic

decisions. Finally, the negative effect of the unemployment rate indicates the strong influence of

the macroeconomic environment on the banks. During the critical years of the 2010s, the

widespread recession in all sectors of Iran’s economy, along with the disorders that have

emerged in the banking system have resulted in an increase in the unemployment rate (as an

indication of the state of recession in Iran) which in turn has led to the reduction of the cost

efficiency due to the lower loan demand and the inability of people to repay their loans.

Among the explanatory variables of the estimated models, the three variables of unemployment

rate, non-productive assets ratio and non-interest income ratio are all among the variables that

have influenced the recession of the 2010s and all of them have negative effects on the cost

efficiency.

Model Robustness Checks

In order to check the robustness of the estimated models, they need to be estimated once

again by a robust regression method. Robust regression is a form of regression method which is

robust and resistant against the outliers. Usually, the differences between these estimations and

the initial main estimations lie in the standard deviations of the estimated coefficients and the

significance of the coefficients after the change of the values of the standard deviations. The

results of the estimations by the robust regression method presented in Table 4 also indicate a

negative effect of the government intervention on the efficiency of the banks, and for the

majority of control variables, the estimation results are the same as those presented in Table 3.

TABLE 4

RESULTS OF MODEL ESTIMATION USING ROBUST REGRESSION

Variables Model(1) Model(2) Model(3) Model(4)

Pinterven -4.75824***

-4.77392***

-4.8418***

-4.77565***

Govpin -5.2473***

-5.27539***

-5.42397***

-6.22008***

HHI 0.167362***

0.184203***

0.184852***

0.217808***

NPR -0.961**

-1.00251**

-0.94348**

-0.98473**

COTRE -0.39415***

-0.33896 -0.34634 -0.33145

Liq 0.252787 **

0.271211**

0.279107**

0.446448***

Size -0.01643 -0.01576 -0.03351

NII -0.27875

INF -0.06566 -0.06661

Unem -0.02988**

-0.03365**

-0.03649**

-0.04019**

Constant 1.136839***

1.292528***

1.333101***

1.539978***

*,

**, and



13 1533-3604-20-3-165

CONCLUSION

In this paper, the cost efficiency of Iran’s banking system (29 selected banks) was

investigated. For this purpose, by applying the translog cost function and estimating it based on

the Greene's approach (2005), the efficiency values of the banks were obtained for the time

period 2008-2016. The average trend of the obtained efficiency values in Iran’s banking system

during the mentioned period has been downward, majorly occurred since 2013 following the

currency crisis as a result of the sanctions. The aforementioned crisis has led to long-term

recession condition, credit crunch and balance sheet disorders in Iran’s banking system. Along

with the mentioned crisis, and during this period, the government intervention in the banking

system has increased in spite of the privatization of most Iranian banks. As expected, this

intervention was much higher in the state-owned banks. Basically, the recession itself will lead to

the reduction of government tax revenues, budget deficits, and excessive borrowing from the

banking system which includes high intervention. Therefore, if the recession continues, this

faulty cycle will be also continuously accompanied by the downward trend of the cost efficiency

of the banking network and in the future, it will face the banking system with more severe crises

such as bankruptcy.

One of the serious challenges of Iran’s banking system, as determined by the estimation

results of two variables HHI (concentration on the loan ratio) and NII (non-interest income) and

their positive and negative effects on the cost efficiency, respectively, is the necessity of the

Iranian banks’ attention to their core activities, namely granting loans and receiving deposits. In

recent years, the increase in the number of enterprises owned by the banks, accompanied by the

banks’ increased focus on the non-interest revenues has led to a reduction in the cost efficiency

of the banks.

In general, the government political intervention in Iran’s banking system has led to the

reduction of the ability of bank managers to achieve efficiency in financial intermediation

operations, the reduction of the motivation of managers to minimize costs considering the market

volatility, the restrictions for the managers to exploit market information for adopting optimal

decisions and finally, rapid changes of managers’ positions which has reduced their

responsiveness. Considering the resulting reduction in the cost efficiency that can threaten the

financial stability of the banks, Iran's economic stability will also be challenged in the future,

because Iran's economic system is bank-centered and more than 80% of the financing comes

from this system.

The main suggestion of this paper is the government's serious determination to decrease its

intervention by reducing the volume of its ownership in the banking system and reduce the use of

bank credits. These should be at the top of the government's economic priorities in order to

prevent further reduction in economic stability. For this purpose, new legislation is required by

the central bank. As an alternative for government financing, it is the development of the debt

market and the use of the market-related securities, rather than borrowing from the banking

system that can be used to control the government's excess liquidity in addition to resolving the

government deficits. The other alternative is the expansion of the tax system in both efficiency

and justice. This can be achieved by optimizing tax rates and focusing on deterrent factors of tax

evasion. In Iran, these two alternatives are among the issues that can be seriously discussed and

revised in the field of implementation.


14 1533-3604-20-3-165

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